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You receive a phone call: the results from routine blood tests show a “low white cell count”. Your doctor explains that more investigations are necessary, perhaps a referral to the haematologists. This might represent something concerning.

But what if this “low white cell count” were completely normal for you, because of a harmless genetic variant shared with millions of people worldwide?

This scenario describes the Duffy null variant: a genetic variation that leads to fewer neutrophils – a type of white blood cell – circulating in the blood. Even though neutrophils fight infections, people with the variant aren’t at higher risk of illness.

The Duffy null variant protects against Plasmodium vivax malaria, a form of the disease that relies on the Duffy antigen to enter red blood cells. In regions of Africa and the Middle East where P. vivax once circulated widely, this variant became common precisely because it blocked the parasite’s entry.

However, when a doctor receives a blood test result, none of this context is present. All they see is a number – often in red – and a reference range which explains what “normal” should be. It is this reference range that guides interpretation of an individual blood test result.

The miscategorisation of neutrophil counts as “low” in people with the Duffy null variant can lead to real-world harms. These include anxiety, as a flagged “low” white cell count can sound like an early warning sign of serious illness.

It also drives avoidable bone marrow biopsies, because clinicians may worry about bone marrow failure or blood cancer and order invasive tests that aren’t actually needed.

People with the variant may be excluded from clinical trials because their neutrophil count appears to fall below strict eligibility cut-offs, despite being completely healthy. And it can even lead to reduced chemotherapy doses, as treatment algorithms automatically lower doses in response to neutrophil numbers, potentially compromising care.

In the past, this pattern was labelled “ethnic neutropenia”, but that term creates its own problems. Race and ethnicity are socially constructed categories, not biological determinants. They’re based on social histories, cultural identities and the ways societies classify people, rather than on precise genetic differences.

Medical practices and technologies can contribute to how socially constructed categories are made and reproduced. People grouped together under the same racial or ethnic label can have very different ancestries and health profiles, so these categories don’t reliably map onto people’s blood counts.

Using them to judge whether a neutrophil level is “normal” risks reinforcing crude groupings and misdiagnosis. What matters is a person’s Duffy status – the genetic factor that actually drives the difference in neutrophil numbers – and blood tests need to be interpreted accordingly.

New research led by Lauren Merz at the University of Michigan and Stephen Hibbs at Queen Mary University of London shows that more than 20% of people with the Duffy null variant are miscategorised as “abnormal” by current reference ranges.

This study created new blood count reference ranges, specific for healthy people with the Duffy null variant. These new Duffy null ranges were consistent for varied participants living in Namibia, Saudi Arabia, the UK and the US, and can be incorporated into clinical practice to support better interpretation and decision making.

What is a reference range?

This isn’t the first time that a reference range – the range of values doctors use to decide whether a test result is “normal” – has worked for some people but not others. Research shows that the HbA1c test, commonly used to diagnose and monitor type 2 diabetes, gives falsely low results in people with a genetic variant widespread among South Asian groups.

These miscategorisations raise a key question: what sort of technology is a reference range? When reference ranges appear in textbooks or on blood test reports, they look like they reveal fixed biological “truths”.

But approaches from science and technology studies encourage us to think differently about what biomedical practices and technologies do. They show how everyday medical practices produce and operationalise racial differences.

To understand this properly, we need to look at what happens in practice. Creating a reference range follows a set process. A laboratory identifies around 120 healthy people from the local population and measures their blood.

The third lowest result (the 2.5th percentile) becomes the “lower limit of normal”, and the third highest (the 97.5th percentile) becomes the “upper limit of normal”. Once a reference range is published, other hospitals can adopt it after checking it against as few as 20 people.

The problem is that these ranges are often built from samples taken mostly from majority groups in a particular region. As a result, they can miss important variation present in minoritised groups and those gaps can translate into mislabelling, misinterpretation and unnecessary worry for patients whose biology simply falls outside the narrow sample used to define “normal”.

While there are sophisticated systems and tools to verify the performance of blood testing machines and assays – the laboratory tests that measure things like cell counts, hormones or chemicals in the blood – there is no standard practice for re-examining reference ranges, even as population demographics change over time and across different locations.

Blood tests that work for everybody

It is time to update “normal” to better encompass the true variety that exists in human populations. This starts with incorporating new blood count reference ranges that adapt to Duffy status.

It also requires us to think carefully and critically about what purportedly neutral technologies and techniques like reference ranges do, and how medicine can work to reproduce racial categories.

This also extends to scrutinising and redesigning other medical practices that normalise one type of blood over another, such as chemotherapy dosing or clinical trial reporting.

We need to examine other everyday tools and practices used by healthcare staff, and ask: What does this technology do? What effects does it create? Does it work well for everybody? These questions illuminate how even routine practices can shape healthcare in ways that benefit some people more than others.

This article is republished from The Conversation, a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: Stephen Hibbs, Queen Mary University of London; Christina Barriteau, Northwestern University, and Kari Lancaster, University of Bath

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Stephen Hibbs receives funding from the Wellcome Trust through a HARP doctoral research fellowship.

Kari Lancaster has received funding from the Australian Research Council and the National Health and Medical Research Council. She has no disclosures to declare in relation to this work.

Christina Barriteau does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.